Evidence map›Paper›PMID 39824881›Full record

ArticleScientific data2025

An image dataset for surveillance of personal protective equipment adherence in healthcare.

Wanzhao Yang, Mary S Kim, Genevieve J Sippel, Aaron H Mun, Kathleen H McCarthy, Beomseok Park, Aleksandra Sarcevic, Marius George Linguraru, Ivan Marsic, Randall S Burd

Abstract readDataset
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific data, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Wanzhao YangDepartment of Electrical and Computer Engineering, Rutgers University, Piscataway, NJ, 08854, USA. wy209@soe.rutgers.edu.ORCID http://orcid.org/0009-0006-4510-8818
Mary S KimDivision of Trauma and Burn Surgery, Children's National Hospital, Washington, DC, 20010, USA.
Genevieve J SippelDivision of Trauma and Burn Surgery, Children's National Hospital, Washington, DC, 20010, USA.
Aaron H MunDivision of Trauma and Burn Surgery, Children's National Hospital, Washington, DC, 20010, USA.
Kathleen H McCarthyDivision of Trauma and Burn Surgery, Children's National Hospital, Washington, DC, 20010, USA.
Beomseok ParkDepartment of Electrical and Computer Engineering, Rutgers University, Piscataway, NJ, 08854, USA.
Aleksandra SarcevicCollege of Computing and Informatics, Drexel University, Philadelphia, PA, 19104, USA.
Marius George LinguraruSheikh Zayed Institute for Pediatric Surgical Innovation, Children's National Hospital, Washington, DC, 20012, USA.
Ivan MarsicDepartment of Electrical and Computer Engineering, Rutgers University, Piscataway, NJ, 08854, USA.
Randall S BurdDivision of Trauma and Burn Surgery, Children's National Hospital, Washington, DC, 20010, USA.

Funding

Intention-aware Recommender System for Improving Trauma Resuscitation OutcomesR01LM011834 · NLM · CHILDREN'S RESEARCH INSTITUTE · PI BURD, RANDALL S., MARSIC, IVAN · 2014 to 2023
$4.7M
Development of a Video-based Personal Protective Equipment Monitoring SystemR01EB032819 · NIBIB · CHILDREN'S RESEARCH INSTITUTE · PI BURD, RANDALL S., LINGURARU, MARIUS GEORGE · 2023 to 2025
$1.5M
DEVELOPMENT OF A VIDEO-BASED PERSONAL PROTECTIVE EQUIPMENT MONITORING SYSTEMR56EB032819 · NIBIB · CHILDREN'S RESEARCH INSTITUTE · PI BURD, RANDALL S., LINGURARU, MARIUS GEORGE · 2022 to 2022
$657k
NIBIB NIH HHS R01 EB032819NIBIB NIH HHS R56 EB032819NLM NIH HHS R01 LM011834U.S. Department of Health & Human Services | NIH | National Institute of Biomedical Imaging and Bioengineering (NIBIB) R01LM011834U.S. Department of Health & Human Services | NIH | National Institute of Biomedical Imaging and Bioengineering (NIBIB) R56EB032819
6 · The paper itself

Abstract

Proper personal protective equipment (PPE) use is critical to prevent disease transmission to healthcare providers, especially those treating patients with a high infection risk. To address the challenge of monitoring PPE usage in healthcare, computer vision has been evaluated for tracking adherence. Existing datasets for this purpose, however, lack a diversity of PPE and nonadherence classes, represent single not multiple providers, and do not depict dynamic provider movement during patient care. We introduce the Resuscitation Room Personal Protective Equipment (R2PPE) dataset that bridges this gap by providing a realistic portrayal of diverse PPE use by multiple interacting individuals in a healthcare setting. This dataset contains 26 videos, 10,034 images and 123,751 bounding box annotations for 17 classes of PPE adherence and nonadherence for eyewear, masks, gowns, and gloves, and one additional head class. Evaluations using newly proposed metrics confirm R2PPE exhibits higher annotation density than three established general-purpose and medical PPE datasets. The R2PPE dataset provides a resource for developing computer vision algorithms for monitoring PPE use in healthcare.

Indexed as

Personal Protective EquipmentHealth PersonnelHumans

Identifiers

PMID39824881
PMCPMC11742010

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.